Reinforcement learning applied to vessel navigation in fast-time simulations.

Detalhes bibliográficos
Ano de defesa: 2020
Autor(a) principal: Andrade, José Amendola Netto
Orientador(a): Não Informado pela instituição
Banca de defesa: Não Informado pela instituição
Tipo de documento: Dissertação
Tipo de acesso: Acesso aberto
Idioma: eng
Instituição de defesa: Biblioteca Digitais de Teses e Dissertações da USP
Programa de Pós-Graduação: Não Informado pela instituição
Departamento: Não Informado pela instituição
País: Não Informado pela instituição
Palavras-chave em Português:
Link de acesso: https://www.teses.usp.br/teses/disponiveis/3/3152/tde-04052021-085708/
Resumo: Fast-time simulations have been proven to be an essential tool for maritime engineering, not only in ship design but also by detecting critical situations and bottlenecks in projects of ports. However, such simulations are not performed by professional pilots and might become a complex task with results not so close to reality. Such issues can present an opportunity for introducing Reinforcement Learning methods in the maritime domain. This work proposes a Reinforcement Learning based solution which is able to automatically generate vessel trajectories in restricted waters under the effect of environment forces. The agent learns by interacting with the simulator and receiving reward signals. It also gives discrete commands in spaced time steps in order to emulate limitations of human piloting. The method evaluates the distributed version of two state-of-art Reinforcement Learning algorithms. It handles channel segments as separate episodes and includes curvature information for anticipating actions. Experiments were run considering realistic scenarios with narrow curved channels where wind and current incidence varies along the trajectory. The novelty of the work is the fact that the solution proposed requires no prior knowledge on dynamic models or predefined line paths to be followed by the ship. It may impact in fast-time simulations by requiring less human effort in trajectories generation. The method adopted keeps a simple representation and can be applied to any port channel configuration that respects local technical regulations.